The most advanced AI retrieval system.
Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
R2R is an advanced AI retrieval system supporting Retrieval-Augmented Generation (RAG) with production-ready features. Built around a RESTful API, R2R offers multimodal content ingestion, hybrid search, knowledge graphs, and comprehensive document management.
R2R also includes a Deep Research API, a multi-step reasoning system that fetches relevant data from your knowledgebase and/or the internet to deliver richer, context-aware answers for complex queries.
# Basic search results = client.retrieval.search(query="What is DeepSeek R1?") # RAG with citations response = client.retrieval.rag(query="What is DeepSeek R1?") # Deep Research RAG Agent response = client.retrieval.agent( message={"role":"user", "content": "What does deepseek r1 imply? Think about market, societal implications, and more."}, rag_generation_config={ "model": "anthropic/claude-3-7-sonnet-20250219", "extended_thinking": True, "thinking_budget": 4096, "temperature": 1, "top_p": None, "max_tokens_to_sample": 16000, }, )
# Quick install and run in light mode pip install r2r export OPENAI_API_KEY=sk-... python -m r2r.serve # Or run in full mode with Docker # git clone [email protected]:SciPhi-AI/R2R.git && cd R2R # export R2R_CONFIG_NAME=full OPENAI_API_KEY=sk-... # docker compose -f compose.full.yaml --profile postgres up -d
For detailed self-hosting instructions, see the self-hosting docs.
demo_2x_comp.mp4
# Install SDK pip install r2r # Python # or npm i r2r-js # JavaScript
from r2r import R2RClient client = R2RClient(base_url="http://localhost:7272")
const { r2rClient } = require('r2r-js'); const client = new r2rClient("http://localhost:7272");
# Ingest sample or your own document client.documents.create(file_path="/path/to/file") # List documents client.documents.list()
- 📁 Multimodal Ingestion: Parse
.txt,.pdf,.json,.png,.mp3, and more - 🔍 Hybrid Search: Semantic + keyword search with reciprocal rank fusion
- 🔗 Knowledge Graphs: Automatic entity & relationship extraction
- 🤖 Agentic RAG: Reasoning agent integrated with retrieval
- 🔐 User & Access Management: Complete authentication & collection system
- Join our Discord for support and discussion
- Submit feature requests or bug reports
- Open PRs for new features, improvements, or documentation